GLM models of the effects of MHC-I functional divergence on fitness
Jacob Roved
5/9/2019
This document presents the R code and output from the GLM models behind the results on the effects of MHC-I functional divergence on fitness in adult great reed warblers reported in the paper:
Roved J., Hansson B., Tarka M., Hasselquist D., & Westerdahl H. (2019). MHC-I functional divergence is positively associated with life span and fitness in male great reed warblers: support for the divergent allele advantage hypothesis.
The data set is available at the datadryad.org repository, the link is specified in the paper mentioned above.
Note: The variable names employed in the models stem from our original analyses. The names of the corresponding variables in the data set that is publicly available at the respository are given below:
LifeSpan = Life span
ToSucLife_1014 = Lifetime no. fledglings
RecruLife_1014 = Lifetime no. recruiting offspring
Total.no.alleles = No. MHC-I alleles
PdistPBR = P-distance PBR
PdistPSS = P-distance PSS
Subset the data.
data.males <- subset(data, Sex=="male")
data.females <- subset(data,Sex=="female")Load R packages.
library(MASS)Function to test for overdispersion in GLM models using Pearson residuals (courtesy of Ben Bolker).
overdisp_fun <- function(model) {
rdf <- df.residual(model)
rp <- residuals(model,type="pearson")
Pearson.chisq <- sum(rp^2)
prat <- Pearson.chisq/rdf
pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}Modeling the effects of MHC-I functional divergence on life span
GLM of life span on total number of alleles and P-distance PBR in males only
fm1_PBR <- glm(LifeSpan~Total.no.alleles+PdistPBR,negative.binomial(theta=5.14632,link="log"),data=data.males[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s1_PBR <- summary(fm1_PBR)
s1_PBR##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data.males[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5050 -0.4432 -0.1066 0.3828 1.2933
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.76368 1.30462 -2.118 0.03755 *
## Total.no.alleles 0.02781 0.01811 1.535 0.12902
## PdistPBR 11.66330 3.67272 3.176 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.3432786)
##
## Null deviance: 28.754 on 75 degrees of freedom
## Residual deviance: 25.249 on 73 degrees of freedom
## AIC: 303.35
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm1_PBR)## chisq ratio rdf p
## 25.0593297 0.3432785 73.0000000 1.0000000par(mfrow=c(2,2))
plot(fm1_PBR)
GLM of life span on total number of alleles and P-distance PSS in males only
fm1_PSS <- glm(LifeSpan~Total.no.alleles+PdistPSS,negative.binomial(theta=5.14632,link="log"),data=data.males[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s1_PSS <- summary(fm1_PSS)
s1_PSS##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data.males[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4432 -0.4743 -0.1057 0.3282 1.3444
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.31280 1.75402 -1.319 0.1914
## Total.no.alleles 0.02288 0.02022 1.132 0.2615
## PdistPSS 9.01796 4.32516 2.085 0.0406 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.3774148)
##
## Null deviance: 28.754 on 75 degrees of freedom
## Residual deviance: 27.094 on 73 degrees of freedom
## AIC: 305.19
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm1_PSS)## chisq ratio rdf p
## 27.5512777 0.3774148 73.0000000 0.9999997par(mfrow=c(2,2))
plot(fm1_PSS)
GLM of life span on total number of alleles and P-distance PBR in females only
fm2_PBR <- glm(LifeSpan~Total.no.alleles+PdistPBR,negative.binomial(theta=5.14632,link="log"),data=data.females[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s2_PBR <- summary(fm2_PBR)
s2_PBR##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data.females[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3148 -0.6869 -0.1884 0.5797 1.8134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.264632 1.786757 0.708 0.481
## Total.no.alleles 0.005697 0.022879 0.249 0.804
## PdistPBR -0.346263 5.370565 -0.064 0.949
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.8213017)
##
## Null deviance: 72.819 on 94 degrees of freedom
## Residual deviance: 72.750 on 92 degrees of freedom
## AIC: 404.93
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm2_PBR)## chisq ratio rdf p
## 75.5597338 0.8213015 92.0000000 0.8930381par(mfrow=c(2,2))
plot(fm2_PBR)
GLM of life span on total number of alleles and P-distance PSS in females only
fm2_PSS <- glm(LifeSpan~Total.no.alleles+PdistPSS,negative.binomial(theta=5.14632,link="log"),data=data.females[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s2_PSS <- summary(fm2_PSS)
s2_PSS##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data.females[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4313 -0.6993 -0.1710 0.4724 1.8661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.179939 2.035643 1.562 0.122
## Total.no.alleles -0.001591 0.022660 -0.070 0.944
## PdistPSS -5.357085 5.362257 -0.999 0.320
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.8063923)
##
## Null deviance: 72.819 on 94 degrees of freedom
## Residual deviance: 71.945 on 92 degrees of freedom
## AIC: 404.13
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm2_PSS)## chisq ratio rdf p
## 74.1880032 0.8063913 92.0000000 0.9129155par(mfrow=c(2,2))
plot(fm2_PSS)
GLM of life span on total number of alleles and P-distance PBR in both sexes
fm3_PBR <- glm(LifeSpan~Total.no.alleles+PdistPBR+Sex+Total.no.alleles:Sex+PdistPBR:Sex,negative.binomial(theta=5.14632,link="log"),data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s3_PBR <- summary(fm3_PBR)
s3_PBR##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPBR + Sex +
## Total.no.alleles:Sex + PdistPBR:Sex, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5050 -0.6573 -0.1750 0.4280 1.8134
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.264632 1.539616 0.821 0.4126
## Total.no.alleles 0.005697 0.019714 0.289 0.7730
## PdistPBR -0.346263 4.627717 -0.075 0.9404
## Sexmale -4.028310 2.322488 -1.734 0.0847 .
## Total.no.alleles:Sexmale 0.022109 0.031166 0.709 0.4791
## PdistPBR:Sexmale 12.009561 6.736310 1.783 0.0765 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.6098127)
##
## Null deviance: 101.638 on 170 degrees of freedom
## Residual deviance: 97.999 on 165 degrees of freedom
## AIC: 708.28
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm3_PBR)## chisq ratio rdf p
## 100.6190635 0.6098125 165.0000000 0.9999804par(mfrow=c(2,2))
plot(fm3_PBR)
GLM of life span on total number of alleles and P-distance PSS in both sexes
fm3_PSS <- glm(LifeSpan~Total.no.alleles+PdistPSS+Sex+Total.no.alleles:Sex+PdistPSS:Sex,negative.binomial(theta=5.14632,link="log"),data=data[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s3_PSS <- summary(fm3_PSS)
s3_PSS##
## Call:
## glm(formula = LifeSpan ~ Total.no.alleles + PdistPSS + Sex +
## Total.no.alleles:Sex + PdistPSS:Sex, family = negative.binomial(theta = 5.14632,
## link = "log"), data = data[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4432 -0.6218 -0.1523 0.3805 1.8661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.179939 1.780045 1.786 0.0759 .
## Total.no.alleles -0.001591 0.019815 -0.080 0.9361
## PdistPSS -5.357085 4.688965 -1.142 0.2549
## Sexmale -5.492737 2.862678 -1.919 0.0567 .
## Total.no.alleles:Sexmale 0.024473 0.032567 0.751 0.4534
## PdistPSS:Sexmale 14.375044 7.249065 1.983 0.0490 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(5.1463) family taken to be 0.6166023)
##
## Null deviance: 101.638 on 170 degrees of freedom
## Residual deviance: 99.039 on 165 degrees of freedom
## AIC: 709.32
##
## Number of Fisher Scoring iterations: 4overdisp_fun(fm3_PSS)## chisq ratio rdf p
## 101.7392809 0.6166017 165.0000000 0.9999717par(mfrow=c(2,2))
plot(fm3_PSS)
Modeling the effects of MHC-I functional divergence on lifetime number of fledgings
GLM of lifetime number of fledgings on total number of alleles and P-distance PBR in males only
fm4_PBR <- glm(ToSucLife_1014~Total.no.alleles+PdistPBR,negative.binomial(theta=1.656555,link="log"),data=data.males[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s4_PBR <- summary(fm4_PBR)
s4_PBR##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data.males[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1104 -0.8979 -0.2296 0.3733 1.6130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.77799 2.36035 -2.024 0.04660 *
## Total.no.alleles 0.04216 0.03285 1.283 0.20344
## PdistPBR 21.95219 6.66919 3.292 0.00154 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.8162788)
##
## Null deviance: 71.194 on 75 degrees of freedom
## Residual deviance: 61.065 on 73 degrees of freedom
## AIC: 548.69
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm4_PBR)## chisq ratio rdf p
## 59.5882720 0.8162777 73.0000000 0.8708043par(mfrow=c(2,2))
plot(fm4_PBR)
GLM of lifetime number of fledgings on total number of alleles and P-distance PSS in males only
fm4_PSS <- glm(ToSucLife_1014~Total.no.alleles+PdistPSS,negative.binomial(theta=1.656555,link="log"),data=data.males[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s4_PSS <- summary(fm4_PSS)
s4_PSS##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data.males[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2487 -0.9171 -0.2981 0.4302 1.9706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.72706 3.21410 -1.160 0.2500
## Total.no.alleles 0.02535 0.03699 0.685 0.4953
## PdistPSS 16.73262 7.94661 2.106 0.0387 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.9075688)
##
## Null deviance: 71.194 on 75 degrees of freedom
## Residual deviance: 66.568 on 73 degrees of freedom
## AIC: 554.19
##
## Number of Fisher Scoring iterations: 6overdisp_fun(fm4_PSS)## chisq ratio rdf p
## 66.2526731 0.9075709 73.0000000 0.6988037par(mfrow=c(2,2))
plot(fm4_PSS)
GLM of lifetime number of fledgings on total number of alleles and P-distance PBR in females only
fm5_PBR <- glm(ToSucLife_1014~Total.no.alleles+PdistPBR,negative.binomial(theta=1.656555,link="log"),data=data.females[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s5_PBR <- summary(fm5_PBR)
s5_PBR##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data.females[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7646 -0.7670 -0.3059 0.3335 2.0569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.19732 2.06239 1.550 0.125
## Total.no.alleles 0.01181 0.02644 0.447 0.656
## PdistPBR -3.38634 6.19942 -0.546 0.586
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.7562186)
##
## Null deviance: 63.653 on 94 degrees of freedom
## Residual deviance: 63.063 on 92 degrees of freedom
## AIC: 618.72
##
## Number of Fisher Scoring iterations: 6overdisp_fun(fm5_PBR)## chisq ratio rdf p
## 69.5720925 0.7562184 92.0000000 0.9608471par(mfrow=c(2,2))
plot(fm5_PBR)
GLM of lifetime number of fledgings on total number of alleles and P-distance PSS in females only
fm5_PSS <- glm(ToSucLife_1014~Total.no.alleles+PdistPSS,negative.binomial(theta=1.656555,link="log"),data=data.females[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s5_PSS <- summary(fm5_PSS)
s5_PSS##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data.females[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7586 -0.7825 -0.3324 0.4298 2.0345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.08582 2.35264 2.162 0.0332 *
## Total.no.alleles 0.00407 0.02605 0.156 0.8762
## PdistPSS -7.90094 6.19254 -1.276 0.2052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.7343895)
##
## Null deviance: 63.653 on 94 degrees of freedom
## Residual deviance: 62.076 on 92 degrees of freedom
## AIC: 617.73
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm5_PSS)## chisq ratio rdf p
## 67.5638913 0.7343901 92.0000000 0.9738731par(mfrow=c(2,2))
plot(fm5_PSS)
GLM of lifetime number of fledgings on total number of alleles and P-distance PBR in both sexes
fm6_PBR <- glm(ToSucLife_1014~Total.no.alleles+PdistPBR+Sex+Total.no.alleles:Sex+PdistPBR:Sex,negative.binomial(theta=1.656555,link="log"),data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s6_PBR <- summary(fm6_PBR)
s6_PBR##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPBR +
## Sex + Total.no.alleles:Sex + PdistPBR:Sex, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1104 -0.8215 -0.2475 0.3559 2.0569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.19732 2.09831 1.524 0.12948
## Total.no.alleles 0.01181 0.02690 0.439 0.66129
## PdistPBR -3.38634 6.30739 -0.537 0.59207
## Sexmale -7.97518 3.12179 -2.555 0.01153 *
## Total.no.alleles:Sexmale 0.03035 0.04194 0.724 0.47029
## PdistPBR:Sexmale 25.33822 9.07946 2.791 0.00588 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.7827894)
##
## Null deviance: 143.78 on 170 degrees of freedom
## Residual deviance: 124.13 on 165 degrees of freedom
## AIC: 1167.4
##
## Number of Fisher Scoring iterations: 6overdisp_fun(fm6_PBR)## chisq ratio rdf p
## 129.1602445 0.7827894 165.0000000 0.9821231par(mfrow=c(2,2))
plot(fm6_PBR)
GLM of lifetime number of fledgings on total number of alleles and P-distance PSS in both sexes
fm6_PSS <- glm(ToSucLife_1014~Total.no.alleles+PdistPSS+Sex+Total.no.alleles:Sex+PdistPSS:Sex,negative.binomial(theta=1.656555,link="log"),data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s6_PSS <- summary(fm6_PSS)
s6_PSS##
## Call:
## glm(formula = ToSucLife_1014 ~ Total.no.alleles + PdistPSS +
## Sex + Total.no.alleles:Sex + PdistPSS:Sex, family = negative.binomial(theta = 1.656555,
## link = "log"), data = data[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2487 -0.8420 -0.3253 0.4339 2.0345
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.085983 2.472325 2.057 0.0412 *
## Total.no.alleles 0.004071 0.027371 0.149 0.8819
## PdistPSS -7.901424 6.507563 -1.214 0.2264
## Sexmale -8.813039 3.917111 -2.250 0.0258 *
## Total.no.alleles:Sexmale 0.021282 0.044408 0.479 0.6324
## PdistPSS:Sexmale 24.634040 9.938732 2.479 0.0142 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.6566) family taken to be 0.8110086)
##
## Null deviance: 143.78 on 170 degrees of freedom
## Residual deviance: 128.64 on 165 degrees of freedom
## AIC: 1171.9
##
## Number of Fisher Scoring iterations: 6overdisp_fun(fm6_PSS)## chisq ratio rdf p
## 133.8165637 0.8110095 165.0000000 0.9641613par(mfrow=c(2,2))
plot(fm6_PSS)
Modeling the effects of MHC-I functional divergence on lifetime number of recruits
GLM of lifetime number of recruits on total number of alleles and P-distance PBR in males only
fm7_PBR <- glm(RecruLife_1014~Total.no.alleles+PdistPBR,negative.binomial(theta=1.40551,link="log"),data=data.males[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s7_PBR <- summary(fm7_PBR)
s7_PBR##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data.males[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7826 -0.7714 -0.2091 0.3456 1.5977
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.69571 3.11922 -3.108 0.00268 **
## Total.no.alleles 0.09970 0.04266 2.337 0.02219 *
## PdistPBR 28.94496 8.73674 3.313 0.00144 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 0.7232984)
##
## Null deviance: 73.051 on 75 degrees of freedom
## Residual deviance: 64.076 on 73 degrees of freedom
## AIC: 290.62
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm7_PBR)## chisq ratio rdf p
## 52.8009289 0.7233004 73.0000000 0.9640887par(mfrow=c(2,2))
plot(fm7_PBR)
GLM of lifetime number of recruits on total number of alleles and P-distance PSS in males only
fm7_PSS <- glm(RecruLife_1014~Total.no.alleles+PdistPSS,negative.binomial(theta=1.40551,link="log"),data=data.males[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s7_PSS <- summary(fm7_PSS)
s7_PSS##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data.males[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8120 -0.7114 -0.1635 0.3208 2.0971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10.26910 4.20981 -2.439 0.0171 *
## Total.no.alleles 0.09372 0.04826 1.942 0.0560 .
## PdistPSS 26.86065 10.33874 2.598 0.0113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 0.8291243)
##
## Null deviance: 73.051 on 75 degrees of freedom
## Residual deviance: 67.128 on 73 degrees of freedom
## AIC: 293.67
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm7_PSS)## chisq ratio rdf p
## 60.5258756 0.8291216 73.0000000 0.8512463par(mfrow=c(2,2))
plot(fm7_PSS)
GLM of lifetime number of recruits on total number of alleles and P-distance PBR in females only
fm8_PBR <- glm(RecruLife_1014~Total.no.alleles+PdistPBR,negative.binomial(theta=1.40551,link="log"),data=data.females[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s8_PBR <- summary(fm8_PBR)
s8_PBR##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPBR, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data.females[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6347 -1.4625 -0.3396 0.3135 2.3327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.53598 3.44587 -0.156 0.877
## Total.no.alleles -0.04161 0.04371 -0.952 0.344
## PdistPBR 5.22055 10.36608 0.504 0.616
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 1.102901)
##
## Null deviance: 113.2 on 94 degrees of freedom
## Residual deviance: 111.5 on 92 degrees of freedom
## AIC: 345.37
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm8_PBR)## chisq ratio rdf p
## 101.4667300 1.1028992 92.0000000 0.2345127par(mfrow=c(2,2))
plot(fm8_PBR)
GLM of lifetime number of recruits on total number of alleles and P-distance PSS in females only
fm8_PSS <- glm(RecruLife_1014~Total.no.alleles+PdistPSS,negative.binomial(theta=1.40551,link="log"),data=data.females[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s8_PSS <- summary(fm8_PSS)
s8_PSS##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPSS, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data.females[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7596 -1.4213 -0.4168 0.3066 2.4245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.10393 3.92662 1.045 0.299
## Total.no.alleles -0.06025 0.04425 -1.362 0.177
## PdistPSS -7.68950 10.33549 -0.744 0.459
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 1.125134)
##
## Null deviance: 113.20 on 94 degrees of freedom
## Residual deviance: 111.23 on 92 degrees of freedom
## AIC: 345.1
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm8_PSS)## chisq ratio rdf p
## 103.5124441 1.1251353 92.0000000 0.1936933par(mfrow=c(2,2))
plot(fm8_PSS)
GLM of lifetime number of recruits on total number of alleles and P-distance PBR in both sexes
fm9_PBR <- glm(RecruLife_1014~Total.no.alleles+PdistPBR+Sex+Total.no.alleles:Sex+PdistPBR:Sex,negative.binomial(theta=1.40551,link="log"),data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s9_PBR <- summary(fm9_PBR)
s9_PBR##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPBR +
## Sex + Total.no.alleles:Sex + PdistPBR:Sex, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7826 -1.3926 -0.2654 0.3280 2.3327
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.53598 3.17268 -0.169 0.8661
## Total.no.alleles -0.04161 0.04024 -1.034 0.3026
## PdistPBR 5.22055 9.54425 0.547 0.5851
## Sexmale -9.15973 4.75842 -1.925 0.0560 .
## Total.no.alleles:Sexmale 0.14132 0.06303 2.242 0.0263 *
## PdistPBR:Sexmale 23.72440 13.77533 1.722 0.0869 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 0.9349556)
##
## Null deviance: 187.69 on 170 degrees of freedom
## Residual deviance: 175.58 on 165 degrees of freedom
## AIC: 635.99
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm9_PBR)## chisq ratio rdf p
## 154.2676589 0.9349555 165.0000000 0.7147116par(mfrow=c(2,2))
plot(fm9_PBR)
GLM of lifetime number of recruits on total number of alleles and P-distance PSS in both sexes
fm9_PSS <- glm(RecruLife_1014~Total.no.alleles+PdistPSS+Sex+Total.no.alleles:Sex+PdistPSS:Sex,negative.binomial(theta=1.40551,link="log"),data=data[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s9_PSS <- summary(fm9_PSS)
s9_PSS##
## Call:
## glm(formula = RecruLife_1014 ~ Total.no.alleles + PdistPSS +
## Sex + Total.no.alleles:Sex + PdistPSS:Sex, family = negative.binomial(theta = 1.40551,
## link = "log"), data = data[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8120 -1.3934 -0.2542 0.3154 2.4245
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.10393 3.69103 1.112 0.2678
## Total.no.alleles -0.06025 0.04159 -1.449 0.1493
## PdistPSS -7.68950 9.71538 -0.791 0.4298
## Sexmale -14.37303 5.90543 -2.434 0.0160 *
## Total.no.alleles:Sexmale 0.15397 0.06725 2.290 0.0233 *
## PdistPSS:Sexmale 34.55016 14.91831 2.316 0.0218 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(1.4055) family taken to be 0.9941722)
##
## Null deviance: 187.69 on 170 degrees of freedom
## Residual deviance: 178.36 on 165 degrees of freedom
## AIC: 638.77
##
## Number of Fisher Scoring iterations: 5overdisp_fun(fm9_PSS)## chisq ratio rdf p
## 164.0383197 0.9941716 165.0000000 0.5065083par(mfrow=c(2,2))
plot(fm9_PSS)
Modeling the effects of MHC-I functional divergence on offspring fledging success
Linear model of lifetime number of fledgings on total number of alleles and P-distance PBR with lifespan as covariate in males only
fm10_PBR <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPBR,data=data.males[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s10_PBR <- summary(fm10_PBR)
s10_PBR##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPBR,
## data = data.males[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.6438 -5.8328 -0.8704 5.4632 23.3595
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -52.5044 29.4221 -1.785 0.0786 .
## LifeSpan 4.7575 0.7195 6.612 5.68e-09 ***
## Total.no.alleles 0.2099 0.4034 0.520 0.6044
## PdistPBR 152.3550 85.6042 1.780 0.0793 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.061 on 72 degrees of freedom
## Multiple R-squared: 0.4644, Adjusted R-squared: 0.4421
## F-statistic: 20.81 on 3 and 72 DF, p-value: 8.194e-10par(mfrow=c(2,2))
plot(fm10_PBR)
Linear model of lifetime number of fledgings on total number of alleles and P-distance PSS with lifespan as covariate in males only
fm10_PSS <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPSS,data=data.males[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s10_PSS <- summary(fm10_PSS)
s10_PSS##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPSS,
## data = data.males[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.6097 -5.4712 -0.6145 5.1370 24.1169
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -38.09164 38.25115 -0.996 0.323
## LifeSpan 5.02654 0.70723 7.107 7e-10 ***
## Total.no.alleles 0.07344 0.43684 0.168 0.867
## PdistPSS 94.07153 96.00763 0.980 0.330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.198 on 72 degrees of freedom
## Multiple R-squared: 0.4482, Adjusted R-squared: 0.4252
## F-statistic: 19.49 on 3 and 72 DF, p-value: 2.356e-09par(mfrow=c(2,2))
plot(fm10_PSS)
Linear model of lifetime number of fledgings on total number of alleles and P-distance PBR with lifespan as covariate in females only
fm11_PBR <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPBR,data=data.females[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s11_PBR <- summary(fm11_PBR)
s11_PBR##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPBR,
## data = data.females[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.1845 -2.1607 0.1229 1.6074 12.2748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.11888 11.25593 0.988 0.326
## LifeSpan 2.83950 0.19055 14.901 <2e-16 ***
## Total.no.alleles 0.06114 0.14379 0.425 0.672
## PdistPBR -37.50437 33.77333 -1.110 0.270
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.965 on 91 degrees of freedom
## Multiple R-squared: 0.7121, Adjusted R-squared: 0.7026
## F-statistic: 75.02 on 3 and 91 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm11_PBR)
Linear model of lifetime number of fledgings on total number of alleles and P-distance PSS with lifespan as covariate in females only
fm11_PSS <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPSS,data=data.females[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s11_PSS <- summary(fm11_PSS)
s11_PSS##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPSS,
## data = data.females[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.3651 -2.0775 0.1081 1.6705 13.0347
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.40047 13.17409 0.865 0.389
## LifeSpan 2.82151 0.19194 14.700 <2e-16 ***
## Total.no.alleles 0.06757 0.14389 0.470 0.640
## PdistPSS -33.30141 34.45899 -0.966 0.336
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.971 on 91 degrees of freedom
## Multiple R-squared: 0.7111, Adjusted R-squared: 0.7016
## F-statistic: 74.68 on 3 and 91 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm11_PSS)
Linear model of lifetime number of fledgings on total number of alleles and P-distance PBR with lifespan as covariate in both sexes
fm12_PBR <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPBR+Sex+Total.no.alleles:Sex+PdistPBR:Sex,data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s12_PBR <- summary(fm12_PBR)
s12_PBR##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPBR +
## Sex + Total.no.alleles:Sex + PdistPBR:Sex, data = data[!is.na(PdistPBR) &
## !is.na(ToSucLife_1014), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.6629 -3.5429 -0.4787 3.1341 27.4032
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.26681 19.53120 0.474 0.6358
## LifeSpan 3.35383 0.28300 11.851 <2e-16 ***
## Total.no.alleles 0.05105 0.24961 0.205 0.8382
## PdistPBR -36.79178 58.63195 -0.628 0.5312
## Sexmale -76.15279 29.39188 -2.591 0.0104 *
## Total.no.alleles:Sexmale 0.29404 0.39225 0.750 0.4546
## PdistPBR:Sexmale 245.28201 85.58624 2.866 0.0047 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.883 on 164 degrees of freedom
## Multiple R-squared: 0.5405, Adjusted R-squared: 0.5236
## F-statistic: 32.15 on 6 and 164 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm12_PBR)
Linear model of lifetime number of fledgings on total number of alleles and P-distance PSS with lifespan as covariate in both sexes
fm12_PSS <- lm(ToSucLife_1014~LifeSpan+Total.no.alleles+PdistPSS+Sex+Total.no.alleles:Sex+PdistPSS:Sex,data=data[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s12_PSS <- summary(fm12_PSS)
s12_PSS##
## Call:
## lm(formula = ToSucLife_1014 ~ LifeSpan + Total.no.alleles + PdistPSS +
## Sex + Total.no.alleles:Sex + PdistPSS:Sex, data = data[!is.na(PdistPSS) &
## !is.na(ToSucLife_1014), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0382 -3.5805 -0.2141 3.2492 29.3120
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.96890 23.26213 0.214 0.831
## LifeSpan 3.44599 0.28785 11.971 <2e-16 ***
## Total.no.alleles 0.06869 0.25489 0.269 0.788
## PdistPSS -21.42623 60.94379 -0.352 0.726
## Sexmale -57.24369 37.35539 -1.532 0.127
## Total.no.alleles:Sexmale 0.13088 0.41867 0.313 0.755
## PdistPSS:Sexmale 165.45598 94.84718 1.744 0.083 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.034 on 164 degrees of freedom
## Multiple R-squared: 0.52, Adjusted R-squared: 0.5024
## F-statistic: 29.61 on 6 and 164 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm12_PSS)
Modeling the effects of MHC-I functional divergence on offspring recruitment success
Linear model of lifetime number of recruits on total number of alleles and P-distance PBR with lifetime number of fledglings as covariate in males only
fm13_PBR <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPBR,data=data.males[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s13_PBR <- summary(fm13_PBR)
s13_PBR##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPBR, data = data.males[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4679 -0.6180 -0.1508 0.5467 3.6382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.48675 3.70735 -1.480 0.1432
## ToSucLife_1014 0.13848 0.01149 12.054 <2e-16 ***
## Total.no.alleles 0.11761 0.04971 2.366 0.0207 *
## PdistPBR 12.80035 10.71313 1.195 0.2361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.12 on 72 degrees of freedom
## Multiple R-squared: 0.7178, Adjusted R-squared: 0.706
## F-statistic: 61.04 on 3 and 72 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm13_PBR)
Linear model of lifetime number of recruits on total number of alleles and P-distance PSS with lifetime number of fledglings as covariate in males only
fm13_PSS <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPSS,data=data.males[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s13_PSS <- summary(fm13_PSS)
s13_PSS##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPSS, data = data.males[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4205 -0.6302 -0.1484 0.5762 3.5749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.13968 4.63216 -1.973 0.05232 .
## ToSucLife_1014 0.13900 0.01088 12.775 < 2e-16 ***
## Total.no.alleles 0.14205 0.05242 2.710 0.00841 **
## PdistPSS 20.22049 11.57660 1.747 0.08496 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.108 on 72 degrees of freedom
## Multiple R-squared: 0.7239, Adjusted R-squared: 0.7124
## F-statistic: 62.92 on 3 and 72 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm13_PSS)
Linear model of lifetime number of recruits on total number of alleles and P-distance PBR with lifetime number of fledglings as covariate in females only
fm14_PBR <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPBR,data=data.females[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s14_PBR <- summary(fm14_PBR)
s14_PBR##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPBR, data = data.females[!is.na(PdistPBR) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3836 -0.7834 -0.1138 0.7974 5.2017
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.31908 4.10214 -1.297 0.198
## ToSucLife_1014 0.21433 0.02052 10.446 <2e-16 ***
## Total.no.alleles -0.08760 0.05224 -1.677 0.097 .
## PdistPBR 19.49656 12.28996 1.586 0.116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.439 on 91 degrees of freedom
## Multiple R-squared: 0.5521, Adjusted R-squared: 0.5373
## F-statistic: 37.39 on 3 and 91 DF, p-value: 7.743e-16par(mfrow=c(2,2))
plot(fm14_PBR)
Linear model of lifetime number of recruits on total number of alleles and P-distance PSS with lifetime number of fledglings as covariate in females only
fm14_PSS <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPSS,data=data.females[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s14_PSS <- summary(fm14_PSS)
s14_PSS##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPSS, data = data.females[!is.na(PdistPSS) & !is.na(ToSucLife_1014),
## ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5868 -0.7087 -0.1396 0.7950 5.3625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.52332 4.85235 -0.314 0.7543
## ToSucLife_1014 0.21371 0.02093 10.210 <2e-16 ***
## Total.no.alleles -0.10328 0.05280 -1.956 0.0535 .
## PdistPSS 6.92181 12.70041 0.545 0.5871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.457 on 91 degrees of freedom
## Multiple R-squared: 0.5412, Adjusted R-squared: 0.5261
## F-statistic: 35.78 on 3 and 91 DF, p-value: 2.288e-15par(mfrow=c(2,2))
plot(fm14_PBR)
Linear model of lifetime number of recruits on total number of alleles and P-distance PBR with lifetime number of fledglings as covariate in both sexes
fm15_PBR <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPBR+Sex+Total.no.alleles:Sex+PdistPBR:Sex,data=data[!is.na(PdistPBR)&!is.na(ToSucLife_1014),])
s15_PBR <- summary(fm15_PBR)
s15_PBR##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPBR + Sex + Total.no.alleles:Sex + PdistPBR:Sex, data = data[!is.na(PdistPBR) &
## !is.na(ToSucLife_1014), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7865 -0.8432 -0.1574 0.6730 6.0886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.25247 3.82396 -1.112 0.2677
## ToSucLife_1014 0.16436 0.01121 14.656 <2e-16 ***
## Total.no.alleles -0.08175 0.04885 -1.674 0.0961 .
## PdistPBR 17.42576 11.48167 1.518 0.1310
## Sexmale 1.38591 5.86322 0.236 0.8134
## Total.no.alleles:Sexmale 0.18208 0.07688 2.368 0.0190 *
## PdistPBR:Sexmale -13.49193 17.13914 -0.787 0.4323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.347 on 164 degrees of freedom
## Multiple R-squared: 0.602, Adjusted R-squared: 0.5874
## F-statistic: 41.34 on 6 and 164 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm15_PBR)
Linear model of lifetime number of recruits on total number of alleles and P-distance PSS with lifetime number of fledglings as covariate in both sexes
fm15_PSS <- lm(RecruLife_1014~ToSucLife_1014+Total.no.alleles+PdistPSS+Sex+Total.no.alleles:Sex+PdistPSS:Sex,data=data[!is.na(PdistPSS)&!is.na(ToSucLife_1014),])
s15_PSS <- summary(fm15_PSS)
s15_PSS##
## Call:
## lm(formula = RecruLife_1014 ~ ToSucLife_1014 + Total.no.alleles +
## PdistPSS + Sex + Total.no.alleles:Sex + PdistPSS:Sex, data = data[!is.na(PdistPSS) &
## !is.na(ToSucLife_1014), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9970 -0.8266 -0.1215 0.6371 6.2583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.53677 4.45724 0.120 0.90429
## ToSucLife_1014 0.16279 0.01097 14.846 < 2e-16 ***
## Total.no.alleles -0.10010 0.04900 -2.043 0.04268 *
## PdistPSS 2.49426 11.70647 0.213 0.83154
## Sexmale -7.69698 7.22931 -1.065 0.28858
## Total.no.alleles:Sexmale 0.23085 0.08048 2.869 0.00467 **
## PdistPSS:Sexmale 11.70793 18.39705 0.636 0.52540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.352 on 164 degrees of freedom
## Multiple R-squared: 0.5987, Adjusted R-squared: 0.5841
## F-statistic: 40.79 on 6 and 164 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(fm15_PSS)